store = {}
store['args']={'batch_size': 64, 'scoring_batch_size': 512, 'test_batch_size': 512, 'validation_set_size': 1024, 'early_stopping_patience': 3, 'epochs': 30, 'epoch_samples': 5056, 'num_inference_samples': 20, 'available_sample_k': 40, 'num_iterations': 20, 'no_cuda': False, 'name': 'bald_40_825296', 'seed': 825296, 'log_interval': 20, 'type': 'AcquisitionFunction.bald'}
store['iterations']=[]
store['initial_samples']=[12869, 56174, 57607, 14312, 22884, 36072, 54706, 13124, 58895, 31224, 11549, 58663, 52765, 15084, 41300, 49980, 4745, 33097, 53349, 15268]
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.6138, 'nll': 2.5144824993133543}, 'chosen_samples': [55704, 28651, 3444, 26255, 38537, 6091, 47260, 3553, 30064, 9600, 703, 53666, 50981, 50461, 19868, 59855, 24269, 16024, 28783, 58373, 42683, 50118, 27930, 21857, 34907, 6231, 36951, 48527, 39145, 23441, 23472, 52453, 52153, 36717, 25683, 52611, 47597, 33027, 48525, 22136], 'chosen_samples_score': ['1.0946937', '1.0947922', '1.100162', '1.0952208', '1.0974979', '1.0948955', '1.0972395', '1.107173', '1.1106879', '1.114393', '1.1097672', '1.1095688', '1.1088378', '1.1124244', '1.117336', '1.226774', '1.1481584', '1.2481105', '1.1294578', '1.1253295', '1.1374671', '1.1492083', '1.1748909', '1.199768', '1.1257877', '1.1221075', '1.1375811', '1.156683', '1.1239734', '1.1288222', '1.1344471', '1.1904438', '1.142705', '1.1418593', '1.1448686', '1.1233687', '1.1361938', '1.1344407', '1.187532', '1.1981831']})
store['iterations'].append({'num_epochs': 4, 'test_metrics': {'accuracy': 0.7129, 'nll': 1.5023767364501952}, 'chosen_samples': [21789, 12457, 7003, 33363, 51259, 6462, 57908, 7669, 9337, 35327, 13175, 12239, 24265, 31969, 28328, 38817, 53085, 3585, 33149, 34421, 12464, 46980, 4793, 21215, 13038, 20863, 4985, 44311, 51832, 45386, 57525, 51135, 21598, 27503, 48124, 36431, 45203, 24445, 57115, 57161], 'chosen_samples_score': ['0.923649', '0.92609966', '0.92763674', '0.9311737', '0.9829336', '0.95686036', '0.9555371', '0.934695', '0.9453972', '0.9936764', '0.94779134', '0.95228565', '0.93846846', '0.931315', '0.9387967', '0.94318604', '1.0244837', '0.9366357', '1.0376692', '0.9558427', '0.9888407', '1.1485972', '0.934849', '0.9997459', '0.9762537', '0.97747314', '1.0210521', '0.94902074', '0.94451576', '0.9400627', '0.949364', '1.0455132', '0.9749565', '0.9783859', '0.96449476', '0.9573721', '0.9800496', '0.96424943', '0.9393762', '0.9930122']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.7893, 'nll': 1.2714260272979736}, 'chosen_samples': [31312, 857, 32427, 10463, 8449, 704, 59101, 12342, 32080, 3378, 24300, 34800, 52751, 21224, 20333, 58596, 37491, 44255, 59924, 30782, 9468, 12175, 54848, 24311, 48731, 4315, 8676, 6345, 1239, 41371, 38194, 14377, 13262, 57159, 2000, 51584, 1724, 13460, 39526, 59343], 'chosen_samples_score': ['0.97254056', '0.98809844', '0.973727', '0.98260534', '0.9787055', '0.98445785', '0.9869122', '0.9876439', '0.9875939', '0.9973497', '0.98320687', '0.9966824', '0.9988944', '1.000881', '1.0049918', '1.0135419', '1.0078119', '1.0039399', '1.0066843', '0.99932015', '1.0090034', '1.0104719', '1.0152148', '1.0564573', '1.0161836', '1.0767984', '1.0540113', '1.0531259', '1.1123304', '1.0857066', '1.0224963', '1.0281518', '1.0959964', '1.0845563', '1.0743822', '1.1025577', '1.1371702', '1.026051', '1.1033736', '1.1056495']})
store['iterations'].append({'num_epochs': 5, 'test_metrics': {'accuracy': 0.8327, 'nll': 0.8977316055297851}, 'chosen_samples': [3040, 52514, 33812, 14062, 13025, 57212, 25180, 6994, 52358, 45047, 41933, 45326, 4799, 38596, 32794, 16860, 46051, 43176, 18631, 14534, 58249, 11619, 44101, 21776, 32537, 51313, 42707, 31184, 34685, 52971, 33583, 31301, 32519, 57271, 27031, 34608, 27738, 54490, 42101, 48384], 'chosen_samples_score': ['0.8303442', '0.8321846', '0.83178574', '0.83115023', '0.8316384', '0.8319037', '0.83062786', '0.83279157', '0.8398937', '0.8380847', '0.83909917', '0.8387929', '0.8370853', '0.83300406', '0.84242076', '0.8741903', '0.9299208', '0.9100169', '0.8540423', '0.85718334', '0.89546406', '0.8494931', '0.8518718', '0.8601702', '0.8765058', '0.85581183', '0.8677279', '0.86488706', '0.9652904', '0.8865978', '1.0161076', '0.8573083', '0.91796523', '0.9459279', '0.88774586', '0.8935154', '0.8819552', '0.89237165', '0.8809593', '0.84977376']})
store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9112, 'nll': 0.6551979269981384}, 'chosen_samples': [51464, 43946, 39304, 20467, 37643, 36008, 31626, 49316, 57334, 49683, 55878, 18003, 32880, 46412, 9614, 49091, 16210, 718, 14394, 52462, 34819, 43711, 22083, 6920, 49406, 24424, 32387, 17654, 36450, 29530, 31488, 51492, 31090, 21315, 50937, 59418, 55128, 42467, 40654, 4784], 'chosen_samples_score': ['0.96619534', '0.9664906', '0.96811956', '0.9690445', '0.9698802', '0.9750409', '0.97001827', '0.9752214', '0.97314113', '0.9812633', '0.98187304', '0.9703489', '0.97441024', '0.98203063', '1.0533438', '1.0019177', '1.0654273', '0.99981314', '0.99903774', '0.9991639', '1.0448037', '1.0425456', '1.0150746', '1.0572118', '1.0321519', '1.0340585', '1.004002', '0.9849227', '1.0255244', '0.9871394', '0.9880133', '1.0677215', '0.99531376', '1.0768842', '1.0209968', '1.0013632', '1.0600543', '1.0459105', '1.0096622', '1.191769']})
store['iterations'].append({'num_epochs': 6, 'test_metrics': {'accuracy': 0.9146, 'nll': 0.5764923126220703}, 'chosen_samples': [28371, 749, 32268, 22364, 50403, 576, 3128, 31753, 16190, 28784, 9979, 57124, 48512, 16011, 14281, 30884, 20005, 34445, 19038, 52218, 37738, 19258, 8443, 9180, 49890, 33871, 2064, 41432, 41108, 44438, 52720, 28199, 36744, 39834, 54687, 6446, 44998, 26184, 5129, 8851], 'chosen_samples_score': ['0.8295205', '0.83051735', '0.8306364', '0.83233297', '0.8332152', '0.83621186', '0.84013027', '0.8365226', '0.8423953', '0.8470729', '0.847707', '0.8516035', '0.8627826', '0.8525331', '0.83695585', '0.84350467', '0.854688', '0.8670444', '0.8683442', '0.8720941', '0.874244', '0.90312994', '0.92724806', '0.89313096', '0.87915796', '0.8916314', '0.8781914', '0.8826744', '0.87249863', '0.90995127', '0.927014', '0.87888855', '1.0222328', '0.9306392', '0.8731491', '0.9607284', '0.91460544', '0.8727264', '0.92112386', '0.8801402']})
store['iterations'].append({'num_epochs': 7, 'test_metrics': {'accuracy': 0.9322, 'nll': 0.4862524782180786}, 'chosen_samples': [44308, 41585, 52582, 43609, 3204, 13540, 43043, 59726, 50054, 25879, 8228, 43012, 4384, 7924, 21001, 42004, 28693, 16882, 491, 14697, 59731, 20037, 8458, 36268, 24589, 47914, 21348, 17065, 26405, 39329, 52747, 31124, 3676, 55893, 11797, 39668, 21947, 29153, 10800, 20171], 'chosen_samples_score': ['0.8481971', '0.8483187', '0.8555535', '0.8647997', '0.8570483', '0.86468613', '0.86150646', '0.8632102', '0.862989', '0.8608227', '0.86590344', '0.911178', '0.8992466', '0.8991694', '0.8881379', '0.86613977', '0.88163316', '0.876148', '0.9163228', '0.8727698', '0.9179588', '0.8719015', '0.87897235', '0.9200647', '0.8915636', '0.91356266', '0.9052231', '0.9190719', '0.9218562', '0.8664838', '0.9228267', '0.96576524', '0.92364293', '0.9350323', '0.9563119', '0.96806777', '0.9719311', '0.9569509', '0.95134276', '0.92299455']})
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store['iterations'].append({'num_epochs': 8, 'test_metrics': {'accuracy': 0.9564, 'nll': 0.354924245929718}, 'chosen_samples': [39411, 14896, 15892, 3374, 23059, 32747, 39865, 42503, 8714, 34520, 37373, 7984, 5315, 39818, 53872, 42078, 30326, 31650, 30692, 37383, 59314, 13538, 29938, 14793, 4185, 32776, 41433, 31738, 42774, 28633, 29179, 16027, 50086, 39561, 28192, 13259, 45887, 22437, 34328, 19356], 'chosen_samples_score': ['0.836024', '0.836619', '0.8369693', '0.8370533', '0.83766353', '0.8393439', '0.8868453', '0.8433063', '0.87565714', '0.9427388', '0.8937974', '0.9153419', '0.8515552', '1.1115808', '0.914658', '0.8643967', '0.8678953', '0.85197467', '0.84529656', '0.890581', '0.91586536', '0.89375603', '0.9018774', '0.8403533', '0.8751603', '0.84890413', '0.85720944', '0.9119446', '0.9050157', '1.0202379', '0.87950623', '0.8502901', '0.85407627', '0.9325649', '0.9320713', '0.8404237', '0.8514553', '0.8867495', '0.93097', '0.8989557']})
store['iterations'].append({'num_epochs': 11, 'test_metrics': {'accuracy': 0.9611, 'nll': 0.3404680484771728}, 'chosen_samples': [43898, 52914, 46300, 7833, 14355, 15832, 3810, 13333, 19188, 15913, 9559, 3367, 57728, 4153, 44364, 59720, 49563, 45533, 57683, 32814, 11403, 5842, 10014, 48380, 50572, 38698, 44123, 33162, 59681, 13376, 54954, 48973, 11292, 23733, 28183, 52294, 31014, 49525, 4822, 53508], 'chosen_samples_score': ['0.89147514', '0.8916829', '0.89416426', '0.8924616', '0.896272', '1.0724498', '0.9873122', '0.91512275', '0.9792932', '0.9040841', '0.9274306', '1.0321537', '0.90301746', '0.96241134', '0.92469424', '0.94848317', '0.9957212', '0.91284055', '0.9889503', '1.0050999', '0.9303341', '0.92816913', '0.9477092', '0.9059647', '0.91214854', '0.9778637', '0.89973044', '0.99716395', '0.9167329', '0.9604846', '0.9036038', '0.9565502', '0.9337497', '0.932237', '0.92641866', '1.0356696', '1.0201824', '1.0146646', '0.8969735', '0.9769123']})
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store['iterations'].append({'num_epochs': 14, 'test_metrics': {'accuracy': 0.9669, 'nll': 0.29335791597366334}, 'chosen_samples': [49692, 2450, 9118, 53019, 7308, 23016, 3010, 36055, 56006, 39355, 48297, 47036, 8867, 15494, 22607, 34942, 58316, 6608, 8772, 3268, 11767, 33856, 30658, 6418, 5298, 1674, 43811, 50236, 9490, 45590, 22139, 57972, 43670, 49734, 54932, 52808, 8978, 36417, 31197, 46379], 'chosen_samples_score': ['0.89644486', '0.8969133', '0.8972298', '0.89834464', '0.8989211', '0.900918', '0.9018094', '0.93871385', '0.94123536', '0.99291945', '0.9194867', '0.98247135', '0.9650259', '0.9138338', '1.0624597', '0.9340918', '1.038249', '0.90486723', '0.93985516', '1.0105705', '0.96593', '0.9048389', '0.92204106', '1.1000466', '1.0208783', '1.0287188', '1.0123131', '0.9162721', '0.94904274', '0.97187984', '0.90876883', '0.9036784', '0.91728914', '0.9666542', '1.1326832', '1.0207531', '0.9551874', '1.0465796', '1.0228215', '0.9154518']})
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